1. New Directions in Pre-Syndromic and Subpopulation Health Surveillance
Daniel B. Neill, Ph.D.
Associate Professor of Information Systems
Director, Event and Pattern Detection Laboratory
Carnegie Mellon University
Visiting Professor of Urban Analytics
New York University
Personal Website:
EPD Lab Website:

2. (NYC DOHMH, NC DOH, NC DETECT)
Syndromic surveillance (current state of the art):
Map cases to known, pre-existing syndrome categories
Monitor aggregate counts to find clusters in space and time
Bulk of our work for the past 15 years  new methods for spatial and subset scanning can better combine multiple data streams, detect irregularly-shaped clusters, and analyze complex data.
Pre-syndromic surveillance: a safety net to complement existing systems by discovering novel outbreaks or other emerging patterns that do not correspond to existing syndromes.
Our current approach, the semantic scan, monitors free-text ED chief complaints and detects anomalous clusters.
(NYC DOHMH, NC DOH, NC DETECT)
Contaminated coffee?
Smoke inhalation?
Meningitis exposure?

3. Syndromic surveillance (current state of the art):
Map cases to known, pre-existing syndrome categories
Monitor aggregate counts to find clusters in space and time
Bulk of our work for the past 15 years  new methods for spatial and subset scanning can better combine multiple data streams, detect irregularly-shaped clusters, and analyze complex data.
Each disease case may contain rich data about demographic, geographic, and behavioral risk factors that is lost when aggregating to count data.
Solution: multidimensional scan, not only over space and time, but also over observed demographic and behavioral characteristics, to identify affected subpopulations.
Example: “An outbreak of fatal overdoses among white males 20-49, in a particular suburb, who mix methadone and Xanax.”
Drug overdose surveillance
(NYC & Allegheny County, PA)
Analysis of prescription drug monitoring program data (Kansas DOH)

4. More details on our web site: http://epdlab.heinz.cmu.edu
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